Exploring the Effect on Pharmacy Students’ Empathy Following a Simulation Using 360-Degree Video
Bibliographic record
Abstract
OBJECTIVE: To explore the effects of a simulation using virtual reality 360-degree video on the empathy of pharmacy students. METHODS: First-year pharmacy students participated in a synchronous, in-person simulation using 360-degree video technology. The 4-min immersive video was developed from a patient perspective to improve the learner's understanding and increase empathy for the patient. Empathy was assessed using the Toronto Empathy Questionnaire (TEQ), which was administered presimulation and postsimulation, along with responses to an open-ended reflection question postsimulation. RESULTS: Forty-four students participated in the simulation. A total of 12 students completed the TEQ before and after the simulation. There was no statistically significant change in TEQ scores pre and postsimulation. Researchers analyzed the written takeaway responses of all 44 students who participated in the simulation. Qualitative analysis revealed the following themes: silent struggle, patient-centered focus, safe environment, and communication. CONCLUSION: Despite the lack of significant change in TEQ scores, the qualitative findings from this pilot study revealed 4 key themes. These themes highlighted that, upon reflecting on their participation in the 360-degree video simulation, students understood the key attributes of empathy and recognized the value of providing empathetic care to patients and communication that promotes empathetic care. Further research is required to determine if 360-degree video is an effective method of simulation delivery to enhance empathy in pharmacy students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".